Abstract
Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.
Highlights
In our society there is an increasing concern about helping and assisting people who suffer from motor disabilities
The goal of this research is to use the electrodes of a Brain-Machine Interfaces (BMIs) system in order to implement a supplementary system based on the skill of a user to control EMG signals produced by clenching different areas of the jaw
Our supplementary system should be complemented with an appropriate BMI system, i.e. a menu application that allows a patient to select the rehabilitation strategy desired while a BMI system measures the mental state of the patient in order to evaluate how the selected strategy affects the mental workload of the patient
Summary
In our society there is an increasing concern about helping and assisting people who suffer from motor disabilities Emerging from this concern, each day, different areas of research are focusing their efforts on developing Human-Machine systems to help people suffering from these conditions [1,2]. Spontaneous BMIs study those brainwaves that can be voluntarily controlled by a subject To achieve this control it is usually necessary to have a training period during which the users learn how to control their brain potentials. There are studies focused on generating control commands [8,9] and on the evaluation of the brain response to different external stimulus with diagnosis purposes [10,11,12]. BMIs (both spontaneous and evoked) are used on other topics in the field of human health, such as the measurement of the mental state of a patient (workload, attention level, emotional state,...) [13] or as support systems on rehabilitation processes [14]
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